针对稀疏快速傅里叶变换(Sparse Fast Fourier Transform,SFFT)并行码相位捕获算法抗噪性能较差的问题,提出了一种新的高抗噪性快速捕获算法。该算法依据伪码相关函数峰值唯一的特点,利用降采样快速傅里叶变换(Downsampling Fast Fourie...针对稀疏快速傅里叶变换(Sparse Fast Fourier Transform,SFFT)并行码相位捕获算法抗噪性能较差的问题,提出了一种新的高抗噪性快速捕获算法。该算法依据伪码相关函数峰值唯一的特点,利用降采样快速傅里叶变换(Downsampling Fast Fourier Transform,DFFT)取代了SFFT并行码相位捕获算法中对噪声容忍能力较差的定位循环与估值循环过程来对伪码相位进行捕获,同时对算法参数进行了优化设计。理论分析及仿真结果表明,与已有的SFFT快速捕获算法相比,SFFT-DT(Combination of SFFT and DFFT)捕获算法的计算速度提升了约19%,抗噪性能提升了约5 dB。与经典的FFT捕获算法相比,当两者抗噪性能近似相同(捕获概率大于95%的前提下)时,本文算法计算量比其减少了约43%。展开更多
Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis.However,the fault features are usually learned and classified under all speeds without considering the effects...Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis.However,the fault features are usually learned and classified under all speeds without considering the effects of speed fluctuation.To overcome this deficiency,a novel intelligent defect detection framework based on time-frequency transformation is presented in this work.In the framework,the samples under one speed are employed for training sparse filtering model,and the remaining samples under different speeds are adopted for testing the effectiveness.Our proposed approach contains two stages:1)the time-frequency domain signals are acquired from the mechanical raw vibration data by the short time Fourier transform algorithm,and then the defect features are extracted from time-frequency domain signals by sparse filtering algorithm;2)different defect types are classified by the softmax regression using the defect features.The proposed approach can be employed to mine available fault characteristics adaptively and is an effective intelligent method for fault detection of agricultural equipment.The fault detection performances confirm that our approach not only owns strong ability for fault classification under different speeds,but also obtains higher identification accuracy than the other methods.展开更多
文摘针对稀疏快速傅里叶变换(Sparse Fast Fourier Transform,SFFT)并行码相位捕获算法抗噪性能较差的问题,提出了一种新的高抗噪性快速捕获算法。该算法依据伪码相关函数峰值唯一的特点,利用降采样快速傅里叶变换(Downsampling Fast Fourier Transform,DFFT)取代了SFFT并行码相位捕获算法中对噪声容忍能力较差的定位循环与估值循环过程来对伪码相位进行捕获,同时对算法参数进行了优化设计。理论分析及仿真结果表明,与已有的SFFT快速捕获算法相比,SFFT-DT(Combination of SFFT and DFFT)捕获算法的计算速度提升了约19%,抗噪性能提升了约5 dB。与经典的FFT捕获算法相比,当两者抗噪性能近似相同(捕获概率大于95%的前提下)时,本文算法计算量比其减少了约43%。
基金Project(51675262)supported by the National Natural Science Foundation of ChinaProject(2016YFD0700800)supported by the National Key Research and Development Program of China+2 种基金Project(6140210020102)supported by the Advance Research Field Fund Project of ChinaProject(NP2018304)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2017-IV-0008-0045)supported by the National Science and Technology Major Project
文摘Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis.However,the fault features are usually learned and classified under all speeds without considering the effects of speed fluctuation.To overcome this deficiency,a novel intelligent defect detection framework based on time-frequency transformation is presented in this work.In the framework,the samples under one speed are employed for training sparse filtering model,and the remaining samples under different speeds are adopted for testing the effectiveness.Our proposed approach contains two stages:1)the time-frequency domain signals are acquired from the mechanical raw vibration data by the short time Fourier transform algorithm,and then the defect features are extracted from time-frequency domain signals by sparse filtering algorithm;2)different defect types are classified by the softmax regression using the defect features.The proposed approach can be employed to mine available fault characteristics adaptively and is an effective intelligent method for fault detection of agricultural equipment.The fault detection performances confirm that our approach not only owns strong ability for fault classification under different speeds,but also obtains higher identification accuracy than the other methods.
